首次从理论上定义安全语言生成，揭示其本质不可行性。
Safe Language Generation in the Limit
- 基于学习极限模型，形式化安全语言生成任务
- 证明安全语言生成至少与普通语言识别一样难，均不可能
- 分析不同场景下可解与不可解的边界，为实际应用提供理论警示
近期关于学习极限中的语言识别研究显示，尽管语言识别不可实现，但语言生成是可行的。随着这一基础领域的扩展，我们必须考虑语言生成在现实场景中的影响。本文首次对安全语言生成进行了理论探讨。基于学习极限的计算范式，我们形式化了安全语言识别与生成的任务。证明在该模型下，安全语言识别不可能实现，而安全语言生成至少与（普通的）语言识别一样困难，后者同样不可实现。最后，我们讨论了几种难以处理和可处理的情况。
原文摘要 · Abstract (English)
Recent results in learning a language in the limit have shown that, although language identification is impossible, language generation is tractable. As this foundational area expands, we need to consider the implications of language generation in real-world settings. This work offers the first theoretical treatment of safe language generation. Building on the computational paradigm of learning in the limit, we formalize the tasks of safe language identification and generation. We prove that under this model, safe language identification is impossible, and that safe language generation is at least as hard as (vanilla) language identification, which is also impossible. Last, we discuss several intractable and tractable cases.
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